Update app.py

app.py

@@ -1,302 +1,398 @@
-import os
-import random
-import uuid
-import json
-import time
-import asyncio
-from threading import Thread
-
-import gradio as gr
-import spaces
-import torch
-import numpy as np
-from PIL import Image
-import cv2
-
-from transformers import (
-    Qwen2VLForConditionalGeneration,
-    Qwen2_5_VLForConditionalGeneration,
-    AutoModelForImageTextToText,
-    AutoProcessor,
-    TextIteratorStreamer,
-)
-from transformers.image_utils import load_image
-
-# Constants for text generation
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load VIREX-062225-exp
-MODEL_ID_M = "prithivMLmods/VIREX-062225-exp"
-processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_M,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load DREX-062225-exp
-MODEL_ID_X = "prithivMLmods/DREX-062225-exp"
-processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
-model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_X,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load typhoon-ocr-3b
-MODEL_ID_T = "scb10x/typhoon-ocr-3b"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_T,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load olmOCR-7B-0225-preview
-MODEL_ID_O = "allenai/olmOCR-7B-0225-preview"
-processor_o = AutoProcessor.from_pretrained(MODEL_ID_O, trust_remote_code=True)
-model_o = Qwen2VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_O,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-def downsample_video(video_path):
-    """
-    Downsamples the video to evenly spaced frames.
-    Each frame is returned as a PIL image along with its timestamp.
-    """
-    vidcap = cv2.VideoCapture(video_path)
-    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
-    fps = vidcap.get(cv2.CAP_PROP_FPS)
-    frames = []
-    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
-    for i in frame_indices:
-        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
-        success, image = vidcap.read()
-        if success:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            pil_image = Image.fromarray(image)
-            timestamp = round(i / fps, 2)
-            frames.append((pil_image, timestamp))
-    vidcap.release()
-    return frames
-
-@spaces.GPU
-def generate_image(model_name: str, text: str, image: Image.Image,
-                   max_new_tokens: int = 1024,
-                   temperature: float = 0.6,
-                   top_p: float = 0.9,
-                   top_k: int = 50,
-                   repetition_penalty: float = 1.2):
-    """
-    Generates responses using the selected model for image input.
-    """
-    if model_name == "VIREX-062225-7B-exp":
-        processor = processor_m
-        model = model_m
-    elif model_name == "DREX-062225-7B-exp":
-        processor = processor_x
-        model = model_x
-    elif model_name == "olmOCR-7B-0225-preview":
-        processor = processor_o
-        model = model_o
-    elif model_name == "Typhoon-OCR-3B":
-        processor = processor_t
-        model = model_t
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
-        return
-
-    messages = [{
-        "role": "user",
-        "content": [
-            {"type": "image", "image": image},
-            {"type": "text", "text": text},
-        ]
-    }]
-    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    inputs = processor(
-        text=[prompt_full],
-        images=[image],
-        return_tensors="pt",
-        padding=True,
-        truncation=False,
-        max_length=MAX_INPUT_TOKEN_LENGTH
-    ).to(device)
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        time.sleep(0.01)
-        yield buffer, buffer
-
-@spaces.GPU
-def generate_video(model_name: str, text: str, video_path: str,
-                   max_new_tokens: int = 1024,
-                   temperature: float = 0.6,
-                   top_p: float = 0.9,
-                   top_k: int = 50,
-                   repetition_penalty: float = 1.2):
-    """
-    Generates responses using the selected model for video input.
-    """
-    if model_name == "VIREX-062225-7B-exp":
-        processor = processor_m
-        model = model_m
-    elif model_name == "DREX-062225-7B-exp":
-        processor = processor_x
-        model = model_x
-    elif model_name == "olmOCR-7B-0225-preview":
-        processor = processor_o
-        model = model_o
-    elif model_name == "Typhoon-OCR-3B":
-        processor = processor_t
-        model = model_t
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if video_path is None:
-        yield "Please upload a video.", "Please upload a video."
-        return
-
-    frames = downsample_video(video_path)
-    messages = [
-        {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
-        {"role": "user", "content": [{"type": "text", "text": text}]}
-    ]
-    for frame in frames:
-        image, timestamp = frame
-        messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
-        messages[1]["content"].append({"type": "image", "image": image})
-    inputs = processor.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_dict=True,
-        return_tensors="pt",
-        truncation=False,
-        max_length=MAX_INPUT_TOKEN_LENGTH
-    ).to(device)
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-    generation_kwargs = {
-        **inputs,
-        "streamer": streamer,
-        "max_new_tokens": max_new_tokens,
-        "do_sample": True,
-        "temperature": temperature,
-        "top_p": top_p,
-        "top_k": top_k,
-        "repetition_penalty": repetition_penalty,
-    }
-    thread = Thread(target=model.generate, kwargs=generation_kwargs)
-    thread.start()
-    buffer = ""
-    for new_text in streamer:
-        buffer += new_text
-        buffer = buffer.replace("<|im_end|>", "")
-        time.sleep(0.01)
-        yield buffer, buffer
-
-# Define examples for image and video inference
-image_examples = [
-    ["Convert this page to doc [text] precisely.", "images/3.png"],
-    ["Convert this page to doc [text] precisely.", "images/4.png"],
-    ["Convert this page to doc [text] precisely.", "images/1.png"],
-    ["Convert chart to OTSL.", "images/2.png"]
-]
-
-video_examples = [
-    ["Explain the video in detail.", "videos/2.mp4"],
-    ["Explain the ad in detail.", "videos/1.mp4"]
-]
-
-# Added CSS to style the output area as a "Canvas"
-css = """
-.submit-btn {
-    background-color: #2980b9 !important;
-    color: white !important;
-}
-.submit-btn:hover {
-    background-color: #3498db !important;
-}
-.canvas-output {
-    border: 2px solid #4682B4;
-    border-radius: 10px;
-    padding: 20px;
-}
-"""
-
-# Create the Gradio Interface
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown("# **[Doc VLMs OCR](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
-    with gr.Row():
-        with gr.Column():
-            with gr.Tabs():
-                with gr.TabItem("Image Inference"):
-                    image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
-                    image_upload = gr.Image(type="pil", label="Image")
-                    image_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(
-                        examples=image_examples,
-                        inputs=[image_query, image_upload]
-                    )
-                with gr.TabItem("Video Inference"):
-                    video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
-                    video_upload = gr.Video(label="Video")
-                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(
-                        examples=video_examples,
-                        inputs=[video_query, video_upload]
-                    )
-            with gr.Accordion("Advanced options", open=False):
-                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
-                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
-                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
-                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
-                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
-
-        with gr.Column():
-            with gr.Column(elem_classes="canvas-output"):
-                gr.Markdown("## Result Canvas")
-                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
-                markdown_output = gr.Markdown(label="Formatted Result (Result.Md)")
-
-            model_choice = gr.Radio(
-                choices=["DREX-062225-7B-exp", "olmOCR-7B-0225-preview", "VIREX-062225-7B-exp", "Typhoon-OCR-3B"],
-                label="Select Model",
-                value="DREX-062225-7B-exp"
-            )
-
-            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Doc-VLMs/discussions)")          
-            gr.Markdown("> [DREX-062225-7B-exp](https://huggingface.co/prithivMLmods/DREX-062225-exp): the drex-062225-exp (document retrieval and extraction expert) model is a specialized fine-tuned version of docscopeocr-7b-050425-exp, optimized for document retrieval, content extraction, and analysis recognition. built on top of the qwen2.5-vl architecture.")
-            gr.Markdown("> [VIREX-062225-7B-exp](https://huggingface.co/prithivMLmods/VIREX-062225-exp): the virex-062225-exp (video information retrieval and extraction expert - experimental) model is a fine-tuned version of qwen2.5-vl-7b-instruct, specifically optimized for advanced video understanding, image comprehension, sense of reasoning, and natural language decision-making through cot reasoning.")
-            gr.Markdown("> [Typhoon-OCR-3B](https://huggingface.co/scb10x/typhoon-ocr-3b): a bilingual document parsing model built specifically for real-world documents in thai and english, inspired by models like olmocr, based on qwen2.5-vl-instruction. this model is intended to be used with a specific prompt only.")
-            gr.Markdown("> [olmOCR-7B-0225](https://huggingface.co/allenai/olmOCR-7B-0225-preview): the olmocr-7b-0225-preview model is based on qwen2-vl-7b, optimized for document-level optical character recognition (ocr), long-context vision-language understanding, and accurate image-to-text conversion with mathematical latex formatting. designed with a focus on high-fidelity visual-textual comprehension.")
-            gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")  
-    
-    image_submit.click(
-        fn=generate_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[output, markdown_output]
-    )
-    video_submit.click(
-        fn=generate_video,
-        inputs=[model_choice, video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[output, markdown_output]
-    )
-
-if __name__ == "__main__":
+import os
+import random
+import uuid
+import json
+import time
+import asyncio
+from threading import Thread
+
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image, ImageDraw
+import cv2
+import re
+
+from transformers import (
+    Qwen2_5_VLForConditionalGeneration,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+from transformers.image_utils import load_image
+
+# Constants for text generation
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load Camel-Doc-OCR-062825
+MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
+processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
+model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_M,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Load Qwen2.5-VL-7B-Instruct
+MODEL_ID_X = "Qwen/Qwen2.5-VL-7B-Instruct"
+processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
+model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_X,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Load Qwen2.5-VL-3B-Instruct
+MODEL_ID_T = "Qwen/Qwen2.5-VL-3B-Instruct"
+processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
+model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_T,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+def downsample_video(video_path):
+    """
+    Downsamples the video to evenly spaced frames.
+    Each frame is returned as a PIL image along with its timestamp.
+    """
+    vidcap = cv2.VideoCapture(video_path)
+    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
+    frames = []
+    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
+    for i in frame_indices:
+        vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
+        success, image = vidcap.read()
+        if success:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image)
+            timestamp = round(i / fps, 2)
+            frames.append((pil_image, timestamp))
+    vidcap.release()
+    return frames
+
+def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
+    draw = ImageDraw.Draw(image)
+    for box in bounding_boxes:
+        xmin, ymin, xmax, ymax = box
+        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
+    return image
+
+def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
+    x_scale = original_width / scaled_width
+    y_scale = original_height / scaled_height
+    rescaled_boxes = []
+    for box in bounding_boxes:
+        xmin, ymin, xmax, ymax = box
+        rescaled_box = [
+            xmin * x_scale,
+            ymin * y_scale,
+            xmax * x_scale,
+            ymax * y_scale
+        ]
+        rescaled_boxes.append(rescaled_box)
+    return rescaled_boxes
+
+@spaces.GPU
+def generate_image(model_name: str, text: str, image: Image.Image,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generates responses using the selected model for image input.
+    """
+    if model_name == "Camel-Doc-OCR-062825":
+        processor = processor_m
+        model = model_m
+    elif model_name == "Qwen2.5-VL-7B-Instruct":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Qwen2.5-VL-3B-Instruct":
+        processor = processor_t
+        model = model_t
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": text},
+        ]
+    }]
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        time.sleep(0.01)
+        yield buffer, buffer
+
+@spaces.GPU
+def generate_video(model_name: str, text: str, video_path: str,
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generates responses using the selected model for video input.
+    """
+    if model_name == "Camel-Doc-OCR-062825":
+        processor = processor_m
+        model = model_m
+    elif model_name == "Qwen2.5-VL-7B-Instruct":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Qwen2.5-VL-3B-Instruct":
+        processor = processor_t
+        model = model_t
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
+        return
+
+    if video_path is None:
+        yield "Please upload a video.", "Please upload a video."
+        return
+
+    frames = downsample_video(video_path)
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
+        {"role": "user", "content": [{"type": "text", "text": text}]}
+    ]
+    for frame in frames:
+        image, timestamp = frame
+        messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
+        messages[1]["content"].append({"type": "image", "image": image})
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt",
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer, buffer
+
+@spaces.GPU
+def run_object_detection(model_name: str, image: Image.Image, text_input: str, system_prompt: str,
+                         max_new_tokens: int = 1024,
+                         temperature: float = 0.6,
+                         top_p: float = 0.9,
+                         top_k: int = 50,
+                         repetition_penalty: float = 1.2):
+    if model_name == "Camel-Doc-OCR-062825":
+        processor = processor_m
+        model = model_m
+    elif model_name == "Qwen2.5-VL-7B-Instruct":
+        processor = processor_x
+        model = model_x
+    elif model_name == "Qwen2.5-VL-3B-Instruct":
+        processor = processor_t
+        model = model_t
+    else:
+        return "Invalid model selected.", "", image
+
+    if image is None:
+        return "Please upload an image.", "", image
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": system_prompt},
+                {"type": "text", "text": text_input},
+                {"type": "image", "image": image},
+            ],
+        }
+    ]
+    prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    generation_kwargs = {
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+    generated_ids = model.generate(**inputs, **generation_kwargs)
+    generated_ids_trimmed = generated_ids[:, inputs["input_ids"].shape[1]:]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )[0]
+    pattern = r'\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]'
+    matches = re.findall(pattern, output_text)
+    parsed_boxes = [[int(num) for num in match] for match in matches]
+    original_width, original_height = image.size
+    scaled_boxes = rescale_bounding_boxes(parsed_boxes, original_width, original_height)
+    annotated_image = draw_bounding_boxes(image.copy(), scaled_boxes)
+    return output_text, str(parsed_boxes), annotated_image
+
+# Define examples for image and video inference
+image_examples = [
+    ["Convert this page to doc [text] precisely.", "images/3.png"],
+    ["Convert this page to doc [text] precisely.", "images/4.png"],
+    ["Convert this page to doc [text] precisely.", "images/1.png"],
+    ["Convert chart to OTSL.", "images/2.png"]
+]
+
+video_examples = [
+    ["Explain the video in detail.", "videos/2.mp4"],
+    ["Explain the ad in detail.", "videos/1.mp4"]
+]
+
+# Define examples for object detection
+default_system_prompt = "You are a helpful assistant to detect objects in images. When asked to detect elements based on a description you return bounding boxes for all elements in the form of [xmin, ymin, xmax, ymax] with the values being scaled to 1000 by 1000 pixels. When there are more than one result, answer with a list of bounding boxes in the form of [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ...]."
+object_detection_examples = [
+    ["images/3.png", "Detect all text blocks", default_system_prompt],
+    ["images/4.png", "Find all images", default_system_prompt],
+    ["images/1.png", "Locate the headers", default_system_prompt],
+    ["images/2.png", "Detect the chart", default_system_prompt],
+]
+
+# Added CSS to style the output area as a "Canvas"
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+.canvas-output {
+    border: 2px solid #4682B4;
+    border-radius: 10px;
+    padding: 20px;
+}
+"""
+
+# Create the Gradio Interface
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown("# **[Doc-VLMs-v2-Localization](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
+    with gr.Row():
+        with gr.Column():
+            model_choice = gr.Radio(
+                choices=["Camel-Doc-OCR-062825", "Qwen2.5-VL-7B-Instruct", "Qwen2.5-VL-3B-Instruct"],
+                label="Select Model",
+                value="Camel-Doc-OCR-062825"
+            )
+            with gr.Tabs():
+                with gr.TabItem("Image Inference"):
+                    with gr.Row():
+                        with gr.Column():
+                            image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+                            image_upload = gr.Image(type="pil", label="Image")
+                            image_submit = gr.Button("Submit", elem_classes="submit-btn")
+                            gr.Examples(
+                                examples=image_examples,
+                                inputs=[image_query, image_upload]
+                            )
+                        with gr.Column():
+                            output_image = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
+                            markdown_output_image = gr.Markdown(label="Formatted Result (Result.Md)")
+
+                with gr.TabItem("Video Inference"):
+                    with gr.Row():
+                        with gr.Column():
+                            video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
+                            video_upload = gr.Video(label="Video")
+                            video_submit = gr.Button("Submit", elem_classes="submit-btn")
+                            gr.Examples(
+                                examples=video_examples,
+                                inputs=[video_query, video_upload]
+                            )
+                        with gr.Column():
+                            output_video = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
+                            markdown_output_video = gr.Markdown(label="Formatted Result (Result.Md)")
+
+                with gr.TabItem("Object Detection"):
+                    with gr.Row():
+                        with gr.Column():
+                            input_img = gr.Image(label="Input Image", type="pil")
+                            system_prompt = gr.Textbox(label="System Prompt", value=default_system_prompt)
+                            text_input = gr.Textbox(label="User Prompt")
+                            object_detection_submit = gr.Button("Submit", elem_classes="submit-btn")
+                            gr.Examples(
+                                examples=object_detection_examples,
+                                inputs=[input_img, text_input, system_prompt]
+                            )
+                        with gr.Column():
+                            model_output_text = gr.Textbox(label="Model Output Text")
+                            parsed_boxes = gr.Textbox(label="Parsed Boxes")
+                            annotated_image = gr.Image(label="Annotated Image")
+
+            with gr.Accordion("Advanced options", open=False):
+                max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
+                temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+                top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
+                repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
+
+    image_submit.click(
+        fn=generate_image,
+        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[output_image, markdown_output_image]
+    )
+    video_submit.click(
+        fn=generate_video,
+        inputs=[model_choice, video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[output_video, markdown_output_video]
+    )
+    object_detection_submit.click(
+        fn=run_object_detection,
+        inputs=[model_choice, input_img, text_input, system_prompt, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[model_output_text, parsed_boxes, annotated_image]
+    )
+
+if __name__ == "__main__":
     demo.queue(max_size=30).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)